Mapping of forest types of the tested Reshetka river watershed
In order to estimate the spatial distribution of irrecoverable loss
values on the canopy of tree stands growing in the watershed area of the
Reshetka river, tree species mapping was performed on the basis of the
enlarged features: deciduous, coniferous (the share of other species is
less than 5% of the forest-covered area), mixed with the prevalence of
one of the species (more than 75% of the forest-covered area).
The Sentinel-2A satellite image captured on the 5th of June, 2018 was
used as the initial data. The
choice
of the season stems from the fact that the first half of May is the
period prior to active growth of deciduous phytomass, so it is the best
way possible to determine the share of coniferous trees in the tree
stand structure from the image. Only spectral channels with spatial
resolution of 10 m (near infrared, red, green) were used for
classification. The blue channel was excluded because it is strongly
influenced by atmospheric conditions.
Data processing was performed with the use of the licensed software
package ArcGIS 10.4 and ToolBox application. Sentinel-2A satellite was
launched as a part of Copernicus programme by the European Space Agency
in June 2015. (ESA Introducing Sentinel-2,
http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/
Sentinel-2/Introducing_Sentinel-2). This satellite is equipped with an
optoelectronic spectral sensor for remote surveying of the Earth with a
resolution from 10 to 60 m in the visible, near infrared and short-wave
infrared spectral zones, including 13 spectral channels. It also allows
repeated surveys every 5 days and makes a 290 km wide swath. These
remote sensing data are actively used in mapping and monitoring of
forest species composition (Kurbanov et al., 2018).
To create a training set of samples, the original image in the synthesis
of channels ”red colors” (8-4-3; near IR, red, green) was used. Training
samples were collected using the toolbar ”Image Classification” of
ArcGIS 10.4 software product (developer - ESRI (Environmental Systems
Research Institute, licensed version) and 4 classes for forest cover and
5 classes for non-forest areas were identified (Fig. 5). From 5 to 15
training samples were selected from different parts of the image for
each class. The estimation of class separability by spectral features
was carried out using scattering diagrams. After receiving the training
set of samples, a signature file containing the distribution of pixel
intensity for each class was created. Then maximum likelihood estimation
was applied for performing the classification on the basis of the
obtained signature file. Furthermore, a classification raster was
created and generalized using the majority filter. The resulting map of
vegetation is shown in Fig. 5а.